Monitored machine performance as a maintenance predictor

ABSTRACT

A method, system, and computer program product for predicting abnormal operation of at least one component of a machine is provided. Real time monitoring data from an operating machine is received and monitoring features that are informative of likely abnormal operation are extracted and/or calculated. The monitoring features are applied to a prediction matrix that outputs probabilities of abnormal operation within one or more prediction time horizons. If the output probabilities exceed a threshold probability, then an alert can be output. Maintenance can be automatically scheduled in response to the alert.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. Pat. No.10,558,929, filed on May 31, 2016, which is hereby incorporated byreference in its entirety.

BACKGROUND

Development of prognostic and health management systems that can predictabnormal operations of commercial aircraft components is important foraircraft health management due to the complexity of modern aircraft.

SUMMARY

According to one aspect, a computer-implemented method of predictingcomponent abnormal operation in a machine includes receiving monitoringdata from the machine during operation of the machine. The method alsoincludes computing at least one monitoring feature from the receivedmonitoring data. The method also includes extracting a probability of acomponent operating abnormally from a prediction matrix based on thecomputed at least one monitoring feature. The method also includesscheduling maintenance for the component of the machine upon theextracted probability exceeding a first threshold value.

According to one aspect, a system includes a computer processor and acomputer memory. The computer memory stores a prediction matrix forabnormal operation of a component of a machine. The computer memory alsostores an abnormal operation prediction application. The abnormaloperation prediction application is executable by the computer processorto receive monitoring data from the machine during operation of themachine. The abnormal operation prediction application is alsoexecutable to compute at least one monitoring feature from the receivedmonitoring data. The abnormal operation prediction application is alsoexecutable to extract a probability of a component operating abnormallyfrom the prediction matrix based on the computed at least one monitoringfeature. The abnormal operation prediction application is alsoexecutable to schedule maintenance for the component of the machine uponthe extracted probability exceeding a first threshold value.

According to one aspect, a computer program product for calculating apredicted abnormal operation of a machine includes a computer-readablestorage medium having computer-readable program code embodied therewith.The computer-readable program code is executable by one or more computerprocessors to receive monitoring data from the machine during operationof the machine. The computer-readable program code is also executable tocompute at least one monitoring feature from the received monitoringdata. The computer-readable program code is also executable to extract aprobability of a component operating abnormally from a prediction matrixbased on the computed at least one monitoring feature. Thecomputer-readable program code is also executable to schedulemaintenance for the component of the machine upon the extractedprobability exceeding a first threshold value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram for a system, according to one aspect, forpredicting abnormal operation of a machine;

FIG. 2A is a flow chart for a method, according to one aspect, forextracting historical data from an auxiliary power unit (APU) of anaircraft;

FIG. 2B is a flow chart for a method, according to one aspect, formatching abnormal operation indications in the historical data from FIG.2A with monitoring data in the historical data from FIG. 2A;

FIG. 3 is a flow chart for a method, according to one aspect, fordetermining informative monitoring features, based on the historicaldata from FIG. 2A;

FIG. 4 is a chart illustrating exemplary data for a first monitoringfeature from an APU and showing values of the first monitoring featurefor a period before abnormal operation of the APU and after the cause ofthe abnormal operation has been remedied;

FIG. 5 is a chart illustrating exemplary data for a second monitoringfeature from an APU and showing values of the second monitoring featurefor a period before abnormal operation of the APU and after the cause ofthe abnormal operation has been remedied;

FIG. 6 is a chart illustrating exemplary data for a third monitoringfeature from an APU and showing values of the third monitoring featurefor a period before abnormal operation of the APU and after the cause ofthe abnormal operation has been remedied;

FIG. 7 is a block diagram illustrating at least a portion of anexemplary prediction matrix, according to at least one aspect;

FIG. 8 is a block diagram of a system, according to one aspect, forpredicting abnormal operation of a machine;

FIG. 9 is a flow chart of a method, according to one aspect, forpredicting abnormal operation of a machine;

FIG. 10 is a flow chart of a method, according to one aspect, forgenerating a prediction matrix; and

FIG. 11 is a flow chart of a method, according to one aspect, fordetermining informative monitoring features.

DETAILED DESCRIPTION

In the following, reference is made to aspects presented in thisdisclosure. However, the scope of the present disclosure is not limitedto specific described aspects. Instead, any combination of the followingfeatures and elements, whether related to different aspects or not, iscontemplated to implement and practice contemplated aspects.Furthermore, although aspects disclosed herein may achieve advantagesover other possible solutions or over the prior art, whether or not aparticular advantage is achieved by a given aspect is not limiting ofthe scope of the present disclosure. Thus, the following aspects,features, and advantages are merely illustrative and are not consideredelements or limitations of the appended claims except where explicitlyrecited in a claim(s). Likewise, reference to “the invention” or “thedisclosure” shall not be construed as a generalization of any inventivesubject matter disclosed herein and shall not be considered to be anelement or limitation of the appended claims except where explicitlyrecited in a claim(s).

In aspects described herein, a system that is able to predict auxiliarypower unit (APU) component abnormal operations of commercial aircraft isprovided. In the system, aircraft condition monitoring system (ACMS)data and maintenance message (MMSG) data are used to compute a set ofmonitoring features. The monitoring features are used to monitor theoperating status of and to predict the abnormal operations of APUcomponents. The values of the monitoring features are compared to valuesin a prediction matrix to predict APU component abnormal operations. Theprediction matrix contains a set of monitoring feature value regions,prediction time horizons or periods, and prediction probabilitiescorresponding to each monitoring feature value region and eachprediction period. The prediction matrix is computed from APU historydata. A prediction is conducted by comparing computed APU monitoringfeatures with the prediction matrix (a look-up table method).

FIG. 1 presents a system diagram for an abnormal operation predictionsystem 100 according to one aspect. The abnormal operation predictionsystem 100 includes two parts: a learning module 102 and a predictionmodule 114. In the learning module 102, historical monitoring data 104(e.g., ACMS data) and MMSG data 106 are received by a datapre-processing module 108 from various machines 150 and 160. Themachines 150 and 160 could be a particular type of machine (e.g.,identical machines that are a particular APU model) or a particularclass of machine (e.g., similar machines that are all APUs of aparticular design). The data pre-processing module 108 aligns theHistorical monitoring data 104 with the MMSG data 106 such that eventsrecorded in the Historical monitoring data 104 line up with eventsrecorded in the MMSG data 106. The learning module 102 also includes aninformative features module 110 that identifies monitoring features inthe Historical monitoring data 104 and/or MMSG data 106 that ispredictive of abnormal operation. The learning module 102 also includesa prediction matrix module 112 that computes the prediction matrix thatcontains monitoring feature value regions, prediction time horizons, andprediction probabilities (confidence). The prediction matrix module 112can periodically re-compute the prediction matrix as new MMSG data 106and new Historical monitoring data 104 becomes available.

In the prediction module 114, a data extraction module 118 receivesreal-time monitoring data 116 (e.g., real-time ACMS data) from aparticular one of the machines 160. A compute informative featuresmodule 120 computes values for informative features based on themonitoring features identified by the informative features module 110.In various aspects, the computed values of the monitoring features canbe normalized (e.g., based on an age of the APU) by a normal featureprofiles module 122. A thresholding module 124 determines a monitoringfeature value region of the prediction matrix to be used, and aprediction module 126 performs a look-up operation in the predictionmatrix to determine a probability of abnormal operation. The predictionmodule 114 outputs the abnormal operation probability.

The purpose of the learning module 102 of the system is to findinformative monitoring features from one or more sources 106 that can beused as abnormal operation indicators for a given component of theaircraft APU. Since component abnormal operation information is recordedin maintenance message (MMSG) data 106, the learning module 102 usesboth Historical monitoring data 104 and MMSG data 106 to learninformative monitoring features. The learning module 102 includes a datapre-processing module 108, an informative features module 110, and aprediction matrix module 112.

With reference to FIG. 2 , the data pre-processing module 108 of thelearning module 102 aligns the Historical monitoring data 104 and theMMSG data 106. The Historical monitoring data 104 and the MMSG data 106have specific data formats that are not designed for numerical analysis.Both the Historical monitoring data 104 and the MMSG data 106 mayinclude text, symbolic values, and/or numeric parameters, for example.Therefore, data preprocessing is used to extract relevant data from theHistorical monitoring data 104 and the MMSG data 106 before applyingdata analysis techniques. To perform the data analysis in theinformative feature module 110, two types of data are used: datacollected before a component abnormal operation occurs, which providescondition changing information of a targeted component/system, and datacollected after the component abnormal operation is fixed, whichprovides the normal condition information of the targetedcomponent/system. In various aspects, the time span prior to componentabnormal operation for which data is collected can vary. Such a timespan could be set according to operator policy (e.g., two weeks, threeweeks, four weeks, two months, or three months). Such a time span couldalso be set and/or modified according to historical data. For example,if the Historical monitoring data 104 historically begins to changethree weeks prior to an abnormal operation, then the time span could beset to four weeks to capture the three weeks of data and additional dataas a buffer. The information of an operating condition of a component iscontained in the Historical monitoring data 104 and the information ofthe component abnormal operation is contained in MMSG data 106.Therefore, the Historical monitoring data 104 is first aligned with theMMSG data 106 to obtain the two types of information for the dataanalysis. The data pre-processing module 108 performs two functions:extracting relevant data from the Historical monitoring data 104 and theMMSG data 106, and aligning the Historical monitoring data 104 with theMMSG data 106. FIG. 2A illustrates a flow chart for a process forextracting relevant data from the Historical monitoring data 104 and theMMSG data 106, and FIG. 2B illustrates a flow chart for a process ofmatching the Historical monitoring data 104 with the MMSG data 106.

Referring to FIG. 2A, to extract relevant Historical monitoring data104, raw ACMS data is grouped by tail number in block 202 and thengrouped by abnormal operation message code in block 204. For a given APUcomponent, relevant features are extracted from the raw ACMS data inblock 206 and output as grouped ACMS data 208. Similarly, raw MMSG datais grouped by tail number in block 210 and then by abnormal operationmessage code in block 212, and then output as grouped MMSG data 214.Referring to FIG. 2B, the grouped ACMS data 208 and the grouped MMSGdata 214 are matched by the data alignment process. The ACMS data 208and the MMSG data 214 are matched by tail number in block 220, by MMSGdate code in block 222, and by flight number and airport code in block224. The ACMS data 208 and MMSG data 214 are then matched by minimaltime difference in block 226 to output matched ACMS data 230. Thematched ACMS data 230 includes ACMS monitoring features for before andafter an APU component abnormal operation.

FIG. 3 is a flow chart illustrating a learning process according to oneaspect for the informative features module 110 shown in FIG. 1 .Learning informative monitoring features for abnormal operation of agiven APU component is an important task for predicting abnormaloperation. In various circumstances, the APU includes nineteenmeasurements in ACMS data. For a given MMSG code, not every APUmeasurement in the Historical monitoring data 104 informs abnormaloperation prediction. The informative features module 110 compares twosegments of matched ACMS data 230 to find informative features. One datasegment is obtained from an abnormal operation date up to one monthbefore the abnormal operation date, and is referred to as pre-MMSG data302. The other data segment is from five to eight weeks after theabnormal operation date, and is referred to as post-MMSG data 310. Acomputed value of the monitoring feature (in block 304) of the pre-MMSGdata 302 of the first data segment represents abnormal operatingconditions and a computed value of the monitoring feature (in block 312)of the post-MMSG data 310 of the second data segment represents normaloperating conditions for a given APU MMSG code. For a given MMSG code,during a signal comparison process 314 if a feature signal shows a bigdifference between its pre-MMSG data 302 and its post-MMSG data 310, thefeature is an informative monitoring feature 316 because the featurepresents the difference between normal operating conditions and abnormaloperating conditions. In various aspects, a data segment is obtainedimmediately prior to the abnormal operation, and is referred to asat-abnormal-operation MMSG data 306. A computed value of the monitoringfeature (in block 308) of the at-abnormal-operation MMSG data 306 couldrepresent operating conditions immediately prior to abnormal operations.Three exemplary monitoring features 316 a, 316 b, and 316 c aredescribed in greater detail in the subsequent paragraphs and in FIGS.4-6 .

Different MMSG codes for an APU may have different informativemonitoring features. For illustration purposes, consider an MMSG coderelated to brush wear on an electric starter motor for the APU. Such anabnormal operation (brush wear on an electric starter motor) could beinformed by one or more of three exemplary monitoring features. A firstmonitoring feature 316 a is an engine temperature differential, definedby Equation (1):ΔT(t)=T6(t)−T2(t);  (1)where T6(t) is the measured temperature of the APU engine exhaust andT2(t) is the measured temperature of the APU engine inlet. FIG. 4 showsan exemplary plot 400 of the ΔT(t) monitoring feature 316 a defined byEquation (1) at various engine speeds. At 20% of a maximum APU speed402, the values of the ΔT(t) monitoring feature 316 a for pre-MMSG data404 are significantly higher than for post-MMSG data 406.

A second monitoring feature 316 b is an energy ratio, defined byEquation (2):

$\begin{matrix}{{{{Eng}(t)} = \frac{\Delta\;{T(t)}^{2}}{\Delta\;\theta\;(t)^{2}}};} & (2)\end{matrix}$where ΔT(t) is defined by Equation (1), above, Δθ(t) is a measurement ofAPU angular speed. ΔT(t)² is proportional to the chemical energy in thesystem and Δθ(t)² is proportional to the mechanical energy in thesystem. Thus, the energy ratio monitoring feature 316 b provided byEquation (2) represents the efficiency of energy conversion within thesystem. The system efficiency can change when a component of the APU isexperiencing abnormal operation. FIG. 5 shows an exemplary plot 500 ofthe energy ratio Eng(t) monitoring feature 316 b at various enginespeeds. At 20% of a maximum APU speed 402, the values of the Eng(t)monitoring feature 316 b for pre-MMSG data 504 are significantly higherthan for post-MMSG data 506.

A third monitoring feature 316 c is an electric power value, defined byEquation (3):Pw(t)=l(t)×V(t);  (3)where I(t) is measured APU current output by the electric starter motor(the starter motor also acts as a generator after APU start) and V(t) ismeasured APU voltage output by the electric starter motor. The electricpower Pw(t) monitoring feature 316 c provided by Equation (3) representsthe power consumption of the APU. The electric power Pw(t) monitoringfeature is expected to be different when the electric starter motor isexperiencing abnormal operation. FIG. 6 shows an exemplary plot 600 ofthe electric power Pw(t) monitoring feature 316 c at various enginespeeds. At 15% of a maximum APU speed 602, the values of the Pw(t)monitoring feature 316 c for pre-MMSG data 604 are significantlydifferent from the values for the post-MMSG data 606.

The above-discussed monitoring features are exemplary of monitoringfeatures 316 that may be informative for predicting abnormal operationsof an electric starter motor that has significant brush wear. Othermonitoring features 316 could also be informative for predicting thesame or different abnormal operations of an electric starter motor.Other monitoring features would be informative for predicting abnormaloperations of other components of the APU. For example, variouspressures, pressure ratios, pressure differences, temperatures,temperature ratios, and/or temperature differences could be informativefor various abnormal operations of certain components. In addition, thevarious monitoring features 316 could be most informative at certainoperating conditions. For example, a monitoring feature 316 that definesa pressure across a valve in a duct may be most informative in a rangeof engine speeds or other operating condition of the APU at which thevalve opens or closes.

In various aspects, the values of the monitored features are normalizedto account for an amount of use of the APU. As FIGS. 4-6 demonstrate,the values of the informative monitoring features can change as the APUis used and approaches a life cycle limit (at which time components ofthe APU are replaced, repaired, and/or refurbished). Normalizing themonitoring features can remove or reduce any dependence of the values ofthe monitoring features on the life cycle of the APU. For example, theΔT(t) monitoring feature defined by Equation (1) can be normalizedaccording to Equation (4):

$\begin{matrix}{{{{f\; t_{tmp}} = {\beta\;{\exp\left\lbrack {{- \alpha} \cdot {r(t)}} \right\rbrack}\;\frac{{\Delta\;{T(t)}} - {\Delta\; T_{norm}}}{\Delta\; T_{norm}}}};}{{{{where}\mspace{14mu}{r(t)}} = \frac{c(t)}{LC}},}} & (4)\end{matrix}$c(t) is a measurement of the usage of the APU at time t, and LC is theusage limit of the APU; ΔT_(norm) is the temperature monitoring featurecomputed from the post-MMSG data (e.g., post-MMSG data 310), which isthe normal profile of the temperature monitoring feature; β is equal to5.0; and α varies depending on the value of c(t). For example, in oneaspect, α is equal to 0.5 if c(t) is less than or equal to one third ofthe usage limit LC; α is equal to 3.0 if c(t) is greater than one thirdof LC and less than two thirds of LC; and α is equal to 1.5 if c(t) isgreater than or equal to two thirds of LC. For example, if the APU usagelimit is five thousand hours and a particular APU has one thousandhours, then c(t) is equal to one fifth of LC, and the value of α is 0.5.As another example, if the APU usage limit is four thousand cycles(i.e., turned on and off) and a particular APU has two thousand cycles,then c(t) is equal to one half of LC, and the value of α is 3.0. Invarious aspects, the normalized temperature monitoring feature ft_(tmp)can be calculated based on values of the ΔT(t) monitoring feature andΔT_(norm) that are determined at a particular engine speed, such as 20%of the maximum operating speed of the APU.

As another example, the energy ratio Eng(t) monitoring feature definedby Equation (2) can be normalized according to Equation (5):

$\begin{matrix}{{{f\; t_{eng}} = {\beta\;{\exp\left\lbrack {{- \alpha} \cdot {r(t)}} \right\rbrack}\;\frac{{{Eng}(t)} - {Eng}_{norm}}{{Eng}_{norm}}}};} & (5)\end{matrix}$where Eng_(norm) is the energy ratio from the post-MMSG data; β is equalto 5.0; and α varies depending on the value of c(t). For example, in oneaspect, α is equal to 0.05 if c(t) is less than or equal to one third ofthe usage limit LC; α is equal to 3.5 if c(t) is greater than one thirdof LC and less than two thirds of LC; and a is equal to 1.5 if c(t) isgreater than or equal to two thirds of LC. In various aspects, thenormalized temperature monitoring feature ft_(eng) can be calculatedbased on values of the energy ratio Eng(t) monitoring feature andEng_(norm) that are determined at a particular engine speed, such as 20%of the maximum operating speed of the APU.

As another example, the electric power Pw(t) monitoring feature definedby Equation (3) can be normalized according to Equation (6):

$\begin{matrix}{{{f\; t_{pw}} = {\beta\;{\exp\left\lbrack {{- \alpha} \cdot {r(t)}} \right\rbrack}\;\frac{{{Pw}(t)} - {Pw}_{norm}}{{Pw}_{norm}}}};} & (6)\end{matrix}$wherein Pw_(norm) is the electric power from the post-MMSG data; β isequal to 5.0; and α varies depending on the value of c(t). For example,in one aspect, α is equal to 7.0 if c(t) is less than or equal to onethird of the usage limit LC; α is equal to 2.0 if c(t) is greater thanone third of LC and less than two thirds of LC; and α is equal to 1.5 ifc(t) is greater than or equal to two thirds of LC. In various aspects,the normalized electric power monitoring feature ft_(pw) can becalculated based on values of the electric power Pw(t) monitoringfeature and Pw_(norm) that are determined at a particular engine speed,such as 15% of the maximum operating speed of the APU.

In one aspect, the values of α and β can be determined experimentally.For example, the values of α and β for the different normalizedmonitoring features can be determined by fitting the selecting valuesthat result in the values of the normalized monitoring features beingessentially constant regardless of the life cycle of a particular APU.In one aspect, the value of β is adjusted to scale the value of aparticular normalized feature to be in a desired dynamic range. Invarious aspects, the value β may be omitted (or set to a value of one)from the equation for a particular normalized feature if the resultingvalues of the normalized feature are in the desired dynamic range. Asdiscussed above, the value of the features changes over time as theusage c(t) of the APU increases. The values of α can be adjusted fordifferent ranges of APU usage c(t) to keep the value of the normalizedfeature in the desired dynamic range. In one aspect, the values of α canbe determined by first examining historical data to compute variationsof the value of the feature for the different usage periods of c(t).Thereafter, values of α can be set that make the range of values for thenormalized feature for the respective usage periods of c(t) fall within(or mostly fall within) the desired dynamic range.

The monitoring features or the normalized monitoring features can beused to build a prediction matrix. Before using the monitoring featuresin the prediction matrix, threshold regions are first determined (e.g.,by the thresholding module 124). In one aspect, threshold regions aredetermined for a particular normalized feature by finding a maximumvalue and a minimum value over all of the abnormal operation data in thehistorical data. A range of values between the maximum and minimum valueis then divided into several sub-regions. For example, the range ofvalues could be divided into three or four sub-regions. In variousaspects, the sub-regions have equal or nearly-equal ranges of values. Invarious other aspects, the sub-regions could be divided in anothermanner. For example, the sub-regions could be divided such that an equalor nearly equal number of abnormal operation instances therein. Theboundary values of the sub-regions define the threshold values of thesub-regions.

FIG. 7 illustrates an exemplary prediction matrix 700 for theabove-discussed electric starter motor brush wear abnormal operationthat includes three exemplary prediction sub-matrices: a firstprediction sub-matrix 702 based on the normalized temperature monitoringfeature ft_(tmp), a second prediction sub-matrix 720 based on thenormalized energy ratio monitoring feature ft_(eng), and a thirdprediction sub-matrix 730 based on the normalized electric powermonitoring feature ft_(pw). With respect to the first exemplaryprediction sub-matrix 702, a first column 704 of the first predictionsub-matrix 702 includes values for the normalized temperature monitoringfeature ft_(tmp) that are divided into three sub-regions. A firstsub-region is arranged in a first row 712 of the first predictionsub-matrix 702 and includes values of the normalized temperaturemonitoring feature ft_(tmp) from a minimum value of 0.8 to values lessthan 1.03. A second sub-region is arranged in a second row 714 of thefirst prediction sub-matrix 702 and includes values of the normalizedtemperature monitoring feature ft_(tmp) greater than or equal to 1.03 tovalues less than 1.26. A third sub-region is arranged in a third row 716of the first prediction sub-matrix 702 and includes values of thenormalized temperature monitoring feature ft_(tmp) greater than or equalto 1.26 to a maximum value of 1.5.

With respect to the second exemplary prediction sub-matrix 720, a firstcolumn 722 of the second prediction sub-matrix 720 includes values forthe normalized energy ratio monitoring feature ft_(eng) that are dividedinto three sub-regions. A first sub-region is arranged in a first row724 of the second prediction sub-matrix 720 and includes values of thenormalized energy ratio monitoring feature ft_(eng) from a minimum valueof 1.8 to values less than 2.33. A second sub-region is arranged in asecond row 726 of the second prediction sub-matrix 720 and includesvalues of the normalized energy ratio monitoring feature ft_(eng)greater than or equal to 2.33 to values less than 2.86. A thirdsub-region is arranged in a third row 728 of the second predictionsub-matrix 720 and includes values of the normalized temperaturemonitoring feature ft_(eng) greater than or equal to 2.86 to a maximumvalue of 3.4.

With respect to the third exemplary prediction sub-matrix 730, a firstcolumn 732 of the third prediction sub-matrix 730 includes values forthe normalized electric power monitoring feature ft_(pw) that aredivided into three sub-regions. A first sub-region is arranged in afirst row 734 of the third prediction sub-matrix 730 and includes valuesof the normalized electric power monitoring feature ft_(pw) from aminimum value of 2.5 to values less than 2.9. A second sub-region isarranged in a second row 736 of the third prediction sub-matrix 730 andincludes values of the normalized electric power monitoring featureft_(pw) greater than or equal to 2.9 to values less than 3.3. A thirdsub-region is arranged in a third row 738 of the third predictionsub-matrix 730 and includes values of the normalized electric powermonitoring feature ft_(pw) greater than or equal to 3.3 to a maximumvalue of 3.8.

The prediction sub-matrices 702, 720, and 730 include columns fordifferent prediction time horizons. In the exemplary prediction matrix700, the three sub-matrices 702, 720, and 730 include three columns forthe same three prediction time horizons: a second column 706 for atwo-week prediction time horizon, a third column 708 for a three-weekprediction time horizon, and a fourth column 710 for a four-weekprediction time horizon. The values in the various prediction timehorizon columns indicate a probability that a particular APU willexperience abnormal operations within the time horizon for a given valueof a normalized monitoring feature. For example, if a real-time value ofthe normalized temperature monitoring feature ft_(tmp) for an APU isequal to 0.9, then the first row 712 of the first prediction sub-matrix702 is used. Accordingly, the first prediction sub-matrix indicates thatthe probability of the APU experiencing abnormal operations due toelectric starter motor brush wear in the next two weeks is equal to 0.38or 38%. The first prediction sub-matrix indicates that the probabilityof the APU experiencing abnormal operations due to electric startermotor brush wear in the next three weeks is equal to 0.54 or 54%. Thefirst prediction sub-matrix indicates that the probability of the APUexperiencing abnormal operations due to electric starter motor brushwear in the next four weeks is equal to 0.87 or 87%.

As another example, if a real-time value of the normalized temperaturemonitoring feature ft_(tmp) for an APU is equal to 1.1, then the secondrow 714 of the first prediction sub-matrix 702 is used. Accordingly, thefirst prediction sub-matrix indicates that the probability of the APUexperiencing abnormal operations due to electric starter motor brushwear in the next two weeks is equal to 0.13 or 13%. The first predictionsub-matrix indicates that the probability of the APU experiencingabnormal operations due to electric starter motor brush wear in the nextthree weeks is equal to 0.21 or 21%. The first prediction sub-matrixindicates that the probability of the APU experiencing abnormaloperations due to electric starter motor brush wear in the next fourweeks is equal to 0.38 or 38%.

As another example, if a real-time value of the normalized temperaturemonitoring feature ft_(tmp) for an APU is equal to 1.45, then the thirdrow 716 of the first prediction sub-matrix 702 is used. Accordingly, thefirst prediction sub-matrix indicates that the probability of the APUexperiencing abnormal operations due to electric starter motor brushwear in the next two weeks is equal to 0.14 or 14%. The first predictionsub-matrix indicates that the probability of the APU experiencingabnormal operations due to electric starter motor brush wear in the nextthree weeks is equal to 0.25 or 25%. The first prediction sub-matrixindicates that the probability of the APU experiencing abnormaloperations due to electric starter motor brush wear in the next fourweeks is equal to 0.43 or 43%.

Probabilities based on the prediction time horizons for the normalizedenergy ratio monitoring feature ft_(eng) in the second predictionsub-matrix 720 and for the normalized electric power monitoring featureft_(pw) in the third prediction sub-matrix 730 can be determined in asimilar manner.

To determine the probabilities at the different prediction time horizonsfor the sub-matrices, the calculated normalized monitoring features forthe historical data (e.g., the pre-MMSG data 302 and the post-MMSG data310) are analyzed to determine the number of instances out of the totalnumber of stored in which a component of the APU experienced abnormaloperation within a particular time horizon when a normalized monitoringfeature was within a particular sub-region of values. For example, withreference to the first prediction sub-matrix 702, the historical datamay include one hundred instances of abnormal operation of an electricstarter motor due to brush wear. As an illustrative example, if theabnormal operation occurred in thirty eight of the one hundred instanceswithin two weeks of the values of the normalized temperature monitoringfeature ft_(tmp) being in the first sub-region between a value equal to0.8 and a value less than 1.03 (row 712), then the probability of anabnormal operation in a two-week time horizon (column 706) is the 0.38or 38% indicated in the first prediction sub-matrix 702. As anotherillustrative example, if the abnormal operation occurred in fifty fourof the one hundred instances within three weeks of the values of thenormalized temperature monitoring feature ft_(tmp) being in the firstsub-region between a value equal to 0.8 and a value less than 1.03 (row712), then the probability of an abnormal operation in a three-week timehorizon (column 708) is the 0.54 or 54% indicated in the firstprediction sub-matrix 702. As yet another illustrative example, if theabnormal operation occurred in eighty seven of the one hundred instanceswithin four weeks of the values of the normalized temperature monitoringfeature ft_(tmp) being in the first sub-region between a value equal to0.8 and a value less than 1.03 (row 712), then the probability of anabnormal operation in a four-week time horizon (column 710) is the 0.87or 87% indicated in the first prediction sub-matrix 702.

The probabilities for the remaining rows of the first predictionsub-matrix 702 can be determined in a similar manner. As an illustrativeexample, if the abnormal operation occurred in thirteen of the onehundred instances within two weeks of the values of the normalizedtemperature monitoring feature ft_(tmp) being in the second sub-regionbetween a value equal to 1.03 and a value less than 1.26 (row 714), thenthe probability of an abnormal operation in a two-week time horizon(column 706) is the 0.13 or 13% indicated in the first predictionsub-matrix 702. As another example, if the abnormal operation occurredin twenty one of the one hundred instances within three weeks of thevalues of the normalized temperature monitoring feature ft_(tmp) beingin the second sub-region between a value equal to 1.03 and a value lessthan 1.26 (row 714), then the probability of an abnormal operation in athree-week time horizon (column 708) is the 0.21 or 21% indicated in thefirst prediction sub-matrix 702. As another example, if the abnormaloperation occurred in thirty eight of the one hundred instances withinthree weeks of the values of the normalized temperature monitoringfeature ft_(tmp) being in the second sub-region between a value equal to1.03 and a value less than 1.26 (row 714), then the probability of anabnormal operation in a four-week time horizon (column 710) is the 0.38or 38% indicated in the first prediction sub-matrix 702.

As another illustrative example, if the abnormal operation occurred infourteen of the one hundred instances within two weeks of the values ofthe normalized temperature monitoring feature ft_(tmp) being in thethird sub-region between a value equal to 1.26 and a value equal to orless than 1.5 (row 716), then the probability of an abnormal operationin a two-week time horizon (column 706) is the 0.14 or 14% indicated inthe first prediction sub-matrix 702. As yet another illustrativeexample, if the abnormal operation occurred in twenty five of the onehundred instances within three weeks of the values of the normalizedtemperature monitoring feature ft_(tmp) being in the third sub-regionbetween a value equal to 1.26 and a value equal to or less than 1.5 (row716), then the probability of an abnormal operation in a three-week timehorizon (column 708) is the 0.25 or 25% indicated in the firstprediction sub-matrix 702. As yet another illustrative example, if theabnormal operation occurred in forty three of the one hundred instanceswithin four weeks of the values of the normalized temperature monitoringfeature ft_(tmp) being in the third sub-region between a value equal to1.26 and a value equal to or less than 1.5 (row 716), then theprobability of an abnormal operation in a four-week time horizon (column710) is the 0.43 or 43% indicated in the first prediction sub-matrix702.

The probabilities for the different time horizons for the otherprediction sub-matrices 720 and 730 can be determined in a similarmanner as described above with reference to the first predictionsub-matrix 702. The three exemplary sub-matrices 702, 720, and 730 inthe exemplary prediction matrix 700 use the same exemplary predictiontime horizons of two weeks (column 706), three weeks (column 708), andfour weeks (column 710). In various aspects, different predictionsub-matrices could have different prediction time horizons that aresuitable for different circumstances.

The prediction matrix 700 can store prediction sub-matrices for variousmonitoring features that are informative of different modes of abnormaloperation for the APU. For example, different modes of abnormaloperation could include brush wear of an electric starter motor orsticking of a valve. The prediction matrix could include at least oneprediction sub-matrix for each of the different modes of abnormaloperation, wherein each of the at least one prediction sub-matrices isrelated to a particular normalized monitoring feature that isinformative for the particular mode of abnormal operation.

After the prediction matrix 700 has been populated with the variousprediction sub-matrices, the prediction matrix 700 can be used topredict abnormal operation of APU components in real time. Real-time ornear-real-time ACMS data from an aircraft can be retrieved, and themonitoring features can be determined and normalized, as discussedabove. In various aspects, the ACMS data could be averaged over a windowof time to eliminate or reduce noise in the ACMS data. The normalizedvalues of the monitoring features can be provided to the predictionmatrix 700, and the prediction matrix 700 outputs one or moreprobabilities of abnormal operation. Where a value for a singlenormalized monitoring feature is provided to the prediction matrix, asingle probability value (or set of probability values based on thedifferent prediction time horizons) for a given abnormal operation isoutput. In aspects in which a plurality of values for respectivenormalized monitoring features are provided to the prediction matrix forpredicting a particular abnormal operation, the probability values fromthe respective prediction sub-matrices can be statistically analyzed tooutput a probability for the abnormal operation. For example, an averageof the probability values could be output, a maximum value (i.e., amaximum probability) could be output, or a minimum value (i.e., aminimum probability) could be output.

To illustrate such statistical analysis, the three predictionsub-matrices 702, 720, and 730 in FIG. 7 include probabilities forabnormal operation due to electric starter motor brush wear. Supposethat in a particular instance, the real-time or near-real-time ACMS dataresults in a value for the normalized temperature monitoring featureft_(tmp) of 0.9, a value for the normalized energy ratio monitoringfeature ft_(eng) of 2.45, and a value for the normalized electric powermonitoring feature ft_(pw) of 3.4. As discussed above, the firstprediction sub-matrix 702 uses the normalized temperature monitoringfeature ft_(tmp), and the exemplary value of 0.9 relates toprobabilities of abnormal operation of 38% within two weeks, 54% withinthree weeks, and 87% in three weeks (i.e., the first row 712 of thefirst prediction sub-matrix 702). The second prediction sub-matrix 720uses the normalized energy ratio monitoring feature ft_(eng), and theexemplary value of 2.45 relates to probabilities of abnormal operationof 27% within two weeks, 42% within three weeks, and 60% in three weeks(i.e., the second row 726 of the second prediction sub-matrix 720). Thethird prediction sub-matrix 730 uses the normalized electric powermonitoring feature ft_(pw), and the exemplary value of 3.4 relates toprobabilities of abnormal operation of 16% within two weeks, 24% withinthree weeks, and 30% in three weeks (i.e., the third row 738 of thethird prediction sub-matrix 730). As discussed above, in one aspect, aminimum probability value for each time horizon could be output as theprobability of abnormal operation. Based on the above exemplary valuesfor the normalized monitoring features, the minimum probabilities ofabnormal operation would be 16% (from the third prediction sub-matrix730) within two weeks, 21% (from the first prediction sub-matrix 702)within three weeks, and 30% (from the third prediction sub-matrix 730)within four weeks. In another aspect, a maximum probability value foreach time horizon could be output as the probability of abnormaloperation. Based on the above exemplary values for the normalizedmonitoring features, the maximum probabilities of abnormal operationwould be 38% (from the first prediction sub-matrix 702) within twoweeks, 54% (from the first prediction sub-matrix 702) within threeweeks, and 87% (from the first prediction sub-matrix 702) within fourweeks. In another aspect, an average probability value for each timehorizon could be output as the probability of abnormal operation. Basedon the above exemplary values for the normalized monitoring features,the maximum probabilities of abnormal operation would be 27%((38%+27%+16%)/3) within two weeks, 40% ((54%+42%+24%)/3) within threeweeks, and 59% ((87%+60%+30%)/3) within four weeks.

The probability of abnormal operation output by the prediction matrix700 for a particular mode of abnormal operation can be compared to athreshold probability or array of probability thresholds. In at leastone aspect, an array of thresholds could include different thresholdvalues for the different prediction time horizons. For example, in oneexemplary aspect, an array of threshold values include threshold valuesof 20% for a two-week prediction time horizon, 40% for a three-weekprediction time horizon, and 60% for a four-week time horizon. In theevent the probability of abnormal operation output by the predictionmatrix 700 exceeds a threshold amount, then an alert can be generated.With reference to the exemplary array of threshold values, if theprobability of a particular abnormal operation occurring within twoweeks is greater than or equal to 20%, then an alert can be generated.Similarly, if the probability of a particular abnormal operationoccurring within three weeks is greater than or equal to 40%, then analert can be generated. Also, if the probability of a particularabnormal operation occurring within three weeks is greater than or equalto 60%, then an alert can be generated. In at least one aspect,maintenance to repair, replace, and/or refurbish the component(s)predicted to experience abnormal operation can be automaticallyscheduled in response to the alert.

FIG. 8 illustrates a system 800 for predicting abnormal operation of anAPU or a component of an APU according to one aspect. The system 800includes a computer processor 802 and computer memory 804. The computermemory 804 stores a prediction matrix 806 (e.g., the exemplaryprediction matrix 700 illustrated in FIG. 7 ). The computer memory 804also includes an abnormal operation prediction application 808 that,when executed on the computer processor 802 determines a probability orprobabilities of abnormal operation of the APU based on real-time ornear-real-time ACMS data. The computer memory 804 also includes aprediction matrix generating application 810 that can generate theprediction matrix 806. The computer memory 804 also stores a monitoringfeature generation application 812 that can generate and/or identifymonitoring features that are informative of abnormal operations of theAPU. The system 800 also includes an input 814 and an output 816. Theinput 814 can receive historical ACMS data (e.g., the Historicalmonitoring data 104 in FIG. 1 ) and historical MMSG data (e.g., the MMSGdata 106 data in FIG. 1 ) for use by the prediction matrix generatingapplication 810 to generate the prediction matrix 806 and for use by themonitoring feature generation application 812 to identify and/orgenerate monitoring features that are informative of abnormal operationsof the APU. The input 814 can also receive real-time or near-real-timemonitoring data (e.g., the real-time monitoring data 116 in FIG. 1 ) foruse by the abnormal operation prediction application 808 to determine areal-time probability of abnormal operation of at least a component ofthe APU. The computer processor 802 can output alerts and/or a scheduledmaintenance request via the output 816 upon a determined real-timeprobability exceeding a threshold value.

FIG. 9 illustrates a method 900 according to at least one aspect forpredicting abnormal operation of an APU. For example, the abnormaloperation prediction application 808 in FIG. 8 could execute the method900 on the computer processor 802. In block 902, monitoring data isreceived from the APU. For example, the real-time or near-real-timemonitoring data 116 can be received. In block 904, at least onemonitoring feature is computed from the received monitoring data. Asdiscussed above, the at least one monitoring feature is informative of aparticular abnormal operation and could be a normalized monitoringfeature. In block 906, a probability of the APU operating abnormally isextracted from a prediction matrix based on the at least one monitoringfeature. As discussed above with reference to FIG. 7 , the predictionmatrix could output different probabilities for the same mode ofabnormal operation and/or could output different probabilities fordifferent modes of abnormal operation. In block 908, maintenance for theAPU is automatically scheduled upon the extracted at least oneprobability from the prediction matrix exceeding a threshold value.

FIG. 10 illustrates a method 1000 according to at least one aspect forgenerating a prediction matrix (e.g., the prediction matrix 806. Forexample, the prediction matrix generating application 810 could executethe method 1000 on the computer processor 802. In block 1002, historicalmonitoring data related to instances of abnormal operations for an APUcan be received. In various aspects, the historical monitoring datacould include instances of abnormal operations for a group of APUs ofthe same type that are distributed across a fleet of aircraft. Thereceived data can include the historical monitoring data 104 and thehistorical MMSG data 106, for example. In block 1004, values for atleast one monitoring feature are determined based on the received data,and the range of values is divided into a plurality of valuesub-regions. In block 1006, a probability of abnormal operations for afirst time horizon is calculated by dividing a total number of abnormaloperations by a total number of instances in which the value of thefirst one of the at least one monitoring feature is in the first valuesub-region. In block 1008, a probability of abnormal operations for asecond time horizon is calculated by dividing a total number of abnormaloperations by a total number of instances in which the value of thefirst one of the at least one monitoring feature is in the first valuesub-region. Blocks 1006 and/or 1008 are repeated for every predictiontime horizon and every value sub-region to complete each predictionsub-matrix.

FIG. 11 illustrates a method 1100 according to at least one aspect foridentifying and generating monitoring features based on monitoring data.For example, the monitoring feature generation application 812 couldexecute the method 1100 on the computer processor 802. In block 1102,historical monitoring data related to instances of abnormal operationsof a group of APUs of the same type is received. The received data caninclude the historical monitoring data 104 and the historical MMSG data106, for example. In block 1104, at least one monitoring feature fromthe data is determined based on a difference in value for the at leastone monitoring feature in the data before the abnormal operation andafter the operation. For example, if the difference in value exceeds athreshold amount, then a particular monitoring feature may be consideredinformative for predicting a mode of abnormal operation and thereforedetermined to be a monitoring feature for at least mode of abnormaloperation.

The descriptions of the various aspects have been presented for purposesof illustration, but are not intended to be exhaustive or limited to theaspects disclosed. Many modifications and variations will be apparent tothose of ordinary skill in the art without departing from the scope andspirit of the described aspects. The terminology used herein was chosento best explain the principles of the aspects, the practical applicationor technical improvement over technologies found in the marketplace, orto enable others of ordinary skill in the art to understand the aspectsdisclosed herein.

Aspects described herein may take the form of an entirely hardwareaspect, an entirely software aspect (including firmware, residentsoftware, micro-code, etc.) or an aspect combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module” or “system.”

The aspects described herein may be a system, a method, and/or acomputer program product. The computer program product may include acomputer-readable storage medium (or media) having computer readableprogram instructions (i.e., program code) thereon for causing aprocessor to carry out aspects described herein.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations maybe assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, or either sourcecode or object code written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Smalltalk, C++ or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The computer readable program instructions mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider). In some aspects, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects described herein.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerprogram products according to aspects. It will be understood that eachblock of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousaspects. In this regard, each block in the flowchart or block diagramsmay represent a module, segment, or portion of instructions, whichcomprises one or more executable instructions for implementing thespecified logical function(s). In some alternative implementations, thefunctions noted in the block may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the block diagramsand/or flowchart illustration, and combinations of blocks in the blockdiagrams and/or flowchart illustration, can be implemented by specialpurpose hardware-based systems that perform the specified functions oracts or carry out combinations of special purpose hardware and computerinstructions.

Aspects described herein may be provided to end users through a cloudcomputing infrastructure. Cloud computing generally refers to theprovision of scalable computing resources as a service over a network.More formally, cloud computing may be defined as a computing capabilitythat provides an abstraction between the computing resource and itsunderlying technical architecture (e.g., servers, storage, networks),enabling convenient, on-demand network access to a shared pool ofconfigurable computing resources that can be rapidly provisioned andreleased with minimal management effort or service provider interaction.Thus, cloud computing allows a user to access virtual computingresources (e.g., storage, data, applications, and even completevirtualized computing systems) in “the cloud,” without regard for theunderlying physical systems (or locations of those systems) used toprovide the computing resources.

Typically, cloud computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g. an amount of storage space consumed by auser or a number of virtualized systems instantiated by the user). Auser can access any of the resources that reside in the cloud at anytime, and from anywhere across the Internet. In context of at least oneaspect, a user may access applications (e.g., at least one of theabnormal operation prediction application 808, the prediction matrixgenerating application 810, and the monitoring feature generationapplication 812) or related data available in the cloud. For example,the prediction matrix generating application 810 could execute on acomputing system in the cloud and output the probability of an abnormaloperation. In such a case, the prediction matrix generating application810 could output the prediction matrix 806 and store the outputprediction matrix 806 at a storage location in the cloud. Doing soallows a user to access this information from any computing systemattached to a network connected to the cloud (e.g., the Internet).

In aspects described herein, abnormal operations of an APU can bepredicted based on data that is already monitored. Put differently, noadditional sensors need to be added to the APU to perform the processesdescribed herein. By predicting abnormal operation APU, maintenance ofthe APU can be scheduled in advance of such abnormal operation tominimize any schedule disruptions for the APU and/or the aircraftpowered by the APU.

Aspects have been described herein with reference to an APU. However,the aspects are also applicable to other machinery. For example, theaspects described herein are applicable to any gas turbine engine, suchas a gas turbine engine providing thrust for an aircraft or a gasturbine engine used as a power plant. The aspects described herein couldalso be applicable to piston engines or other complex systems.

While the foregoing is directed to certain aspects, other and furtheraspects may be devised without departing from the basic scope thereof,and the scope thereof is determined by the claims that follow.

What is claimed is:
 1. A computer-implemented method of predictingabnormal operation of a component of a machine, the method comprising:receiving monitoring data from the machine during operation; processingthe monitoring data to determine values for one or more monitoringfeatures that are predictive of abnormal operation of the component,wherein the one or more monitoring features comprise at least a firstmonitoring feature and a second monitoring feature; determining, usingthe values of the one or more monitoring features, a probability ofabnormal operation of the component by extracting the probability from aset of values, wherein the set of values includes a first subset ofvalues for the first monitoring feature and a second subset of valuesfor the second monitoring feature, wherein the first subset of valuesincludes probabilities of abnormal operation for combinations of valuesof the first monitoring feature of the one or more monitoring featuresand time horizons, wherein the second subset of values are related toabnormal operation of one of the component or a second component of themachine, and wherein the second subset of values includes probabilitiesof abnormal operation for combinations of values of one of the firstmonitoring feature or the second monitoring feature and time horizons;and scheduling maintenance for the component responsive to theprobability exceeding a first threshold value.
 2. Thecomputer-implemented method of claim 1, wherein the set of valuescomprises a prediction matrix, and wherein the first set of valuescomprises a first prediction sub-matrix that includes the probabilitiesof abnormal operation for combinations of values of the first monitoringfeature of the one or more monitoring features and time horizons.
 3. Thecomputer-implemented method of claim 2, wherein the second subset ofvalues comprises a second prediction sub-matrix related to the abnormaloperation of the second component of the machine, and wherein the secondprediction sub-matrix includes the probabilities of abnormal operationfor combinations of values of the second monitoring feature and timehorizons.
 4. The computer-implemented method of claim 2, wherein thesecond subset of values comprises a second prediction sub-matrix relatedto abnormal operation of the component, and wherein the secondprediction sub-matrix includes the probabilities of abnormal operationfor combinations of values of the second monitoring feature and timehorizons.
 5. The computer-implemented method of claim 4, whereindetermining the probability of abnormal operation of the componentcomprises: extracting, from the first prediction sub-matrix and thesecond prediction sub-matrix, values of a maximum probability for acombination of a particular time horizon and values of the firstmonitoring feature and the second monitoring feature, respectively. 6.The computer-implemented method of claim 2, further comprising:generating the first prediction sub-matrix, wherein generating the firstprediction sub-matrix includes: receiving historical monitoring datarelated to instances of abnormal operations of at least one of themachine, one or more machines of a same type as the machine, and one ormore machines of a same class as the machine, wherein the instances ofabnormal operations includes data for a first monitoring feature of theone or more monitoring features for: a first period before an abnormaloperation of the component, and a second period after repair of themachine following the abnormal operation; dividing a range of values ofthe one or more monitoring features into a plurality of valuesub-regions; and for each value sub-region: calculating a probability ofabnormal operation for a first time horizon by dividing a total numberof abnormal operations by a total number of instances in which the valueof the first monitoring feature is in the value sub-region; andcalculating a probability of abnormal operation for a second timehorizon by dividing a total number of abnormal operations by the totalnumber of instances in which the value of the first monitoring featureis in the value sub-region.
 7. The computer-implemented method of claim1, further comprising: determining the one or more monitoring features,wherein determining the one or more monitoring features comprises:receiving historical monitoring data related to instances of abnormaloperations of at least one of: the machine, one or more machines of asame type as the machine, and one or more machines of a same class asthe machine, wherein the instances of abnormal operations include datafor: a first period before an abnormal operation of the component, and asecond period after repair of the machine following the abnormaloperation; and computing the one or more monitoring features from thedata based on a difference in value for the one or more monitoringfeatures in the data before the abnormal operation and after theabnormal operation.
 8. A system, comprising: a computer processor; and acomputer memory storing program code executable by the computerprocessor to: receive monitoring data from a machine during operation;determine the one or more monitoring features by: receiving historicalmonitoring data related to instances of abnormal operations of at leastone of: the machine, one or more machines of a same type as the machine,and one or more machines of a same class as the machine, wherein theinstances of abnormal operations include data for: a first period beforean abnormal operation of the component, and a second period after repairof the machine following the abnormal operation; and computing the oneor more monitoring features from the historical monitoring data based ona difference in value for the one or more monitoring features in thehistorical monitoring data before an abnormal operation and after anabnormal operation; process the monitoring data to determine values forthe one or more monitoring features that are predictive of abnormaloperation of a component of the machine; determine, using the values forthe one or more monitoring features, a probability of abnormal operationof the component; and schedule maintenance for the component responsiveto the probability exceeding a first threshold value.
 9. The system ofclaim 8, wherein the computer memory further stores a prediction matrixthat includes probabilities of abnormal operation for the component, andwherein the prediction matrix includes a first prediction sub-matrixthat includes probabilities of abnormal operation for combinations ofvalues of a first monitoring feature of the one or more monitoringfeatures and time horizons.
 10. The system of claim 9, wherein the oneor more monitoring features include a second monitoring feature, whereinthe prediction matrix further includes a second prediction sub-matrixrelated to abnormal operation of a second component of the machine, andwherein the second prediction sub-matrix includes probabilities ofabnormal operation for combinations of values of the second monitoringfeature and time horizons.
 11. The system of claim 9, wherein the one ormore monitoring features include a second monitoring feature, whereinthe prediction matrix further includes a second prediction sub-matrixrelated to abnormal operation of the component, and wherein the secondprediction sub-matrix includes probabilities of abnormal operation forcombinations of values of the second monitoring feature and timehorizons.
 12. The system of claim 11, wherein determining theprobability of abnormal operation of the component comprises:extracting, from the first prediction sub-matrix and the secondprediction sub-matrix, values of a maximum probability for a combinationof a particular time horizon and values of the first monitoring featureand the second monitoring feature, respectively.
 13. The system of claim9, wherein the program code is further (Original) executable by thecomputer processor to generate the first prediction sub-matrix by:receiving historical monitoring data related to instances of abnormaloperations of at least one of the machine, one or more machines of asame type as the machine, and one or more machines of a same class asthe machine, wherein the instances of abnormal operations includes datafor a first monitoring feature of the one or more monitoring featuresfor: a first period before an abnormal operation of a component, and asecond period after repair of the machine following the abnormaloperation; dividing a range of values of the one or more monitoringfeatures into a plurality of value sub-regions; and for each valuesub-region: calculating a probability of abnormal operation for a firsttime horizon by dividing a total number of abnormal operations by atotal number of instances in which the value of the first monitoringfeature is in the value sub-region; and calculating a probability ofabnormal operation for a second time horizon by dividing a total numberof abnormal operations by the total number of instances in which thevalue of the first monitoring feature is in the value sub-region.
 14. Acomputer program product comprising: a non-transitory computer-readablestorage medium having computer-readable program code embodied therewith,the computer-readable program code executable by one or more computerprocessors to: receive monitoring data from a machine during operation;process the monitoring data to determine values for one or moremonitoring features that are predictive of abnormal operation of acomponent of the machine; generate a first subset of values of a set ofvalues by: receiving historical monitoring data related to instances ofabnormal operations of at least one of the machine, one or more machinesof a same type as the machine, and one or more machines of a same classas the machine, wherein the instances of abnormal operations includesdata for a first monitoring feature of the one or more monitoringfeatures for: a first period before an abnormal operation of thecomponent, and a second period after repair of the machine following theabnormal operation; dividing a range of values of the one or moremonitoring features into a plurality of value sub-regions; and for eachvalue sub-region: calculating a probability of abnormal operation for afirst time horizon by dividing a total number of abnormal operations bya total number of instances in which the value of the first monitoringfeature is in the value sub-region; and calculating a probability ofabnormal operation for a second time horizon by dividing a total numberof abnormal operations by the total number of instances in which thevalue of the first monitoring feature is in the value sub-region;determine, using the values of the one or more monitoring features, aprobability of abnormal operation of the component using the set ofvalues; and schedule maintenance for the component responsive to theprobability exceeding a first threshold value.
 15. The computer programproduct of claim 14, wherein determining the probability of abnormaloperation of the component comprises extracting the probability from thefirst set of values, wherein the first set of values comprises aprediction matrix, and wherein the first set of values comprises a firstprediction sub-matrix that includes probabilities of abnormal operationfor combinations of values of a first monitoring feature of the one ormore monitoring features and time horizons.
 16. The computer programproduct of claim 15, wherein the one or more monitoring features includea second monitoring feature, wherein the prediction matrix furtherincludes a second prediction sub-matrix related to abnormal operation ofa second component of the machine, and wherein the second predictionsub-matrix includes probabilities of abnormal operation for combinationsof values of the second monitoring feature and time horizons.
 17. Thecomputer program product of claim 15, wherein the one or more monitoringfeatures include a second monitoring feature, wherein the predictionmatrix further includes a second prediction sub-matrix related toabnormal operation of the component of the machine, and wherein thesecond prediction sub-matrix includes probabilities of abnormaloperation for combinations of values of the second monitoring featureand time horizons.
 18. The computer program product of claim 15, whereinthe computer-readable program code is further executable to determinethe one or more monitoring features by: receiving historical monitoringdata related to instances of abnormal operations of at least one of themachine, one or more machines of a same type as the machine, and one ormore machines of a same class as the machine, wherein the instances ofabnormal operations includes data for: a first period before an abnormaloperation of the component, and a second period after repair of themachine following the abnormal operation; and computing the one or moremonitoring features from the data based on a difference in value for theone or more monitoring features in the data before the abnormaloperation and after the abnormal operation.